CN116759032A - Optimization method for blast furnace steelmaking raw material proportion and application system thereof - Google Patents
Optimization method for blast furnace steelmaking raw material proportion and application system thereof Download PDFInfo
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- 239000002994 raw material Substances 0.000 title claims abstract description 216
- 238000000034 method Methods 0.000 title claims abstract description 72
- 238000005457 optimization Methods 0.000 title claims abstract description 34
- 238000009628 steelmaking Methods 0.000 title claims abstract description 21
- 238000004519 manufacturing process Methods 0.000 claims abstract description 27
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 17
- 238000004140 cleaning Methods 0.000 claims abstract description 14
- 239000000203 mixture Substances 0.000 claims abstract description 14
- 230000002159 abnormal effect Effects 0.000 claims abstract description 7
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 42
- 230000008569 process Effects 0.000 claims description 39
- 238000004364 calculation method Methods 0.000 claims description 29
- 238000005245 sintering Methods 0.000 claims description 24
- 229910052742 iron Inorganic materials 0.000 claims description 21
- 239000000126 substance Substances 0.000 claims description 14
- 239000000463 material Substances 0.000 claims description 11
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 238000013500 data storage Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 description 37
- 238000010586 diagram Methods 0.000 description 11
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 239000004615 ingredient Substances 0.000 description 2
- 229910052500 inorganic mineral Inorganic materials 0.000 description 2
- 239000011707 mineral Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 239000000843 powder Substances 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000000571 coke Substances 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 239000008188 pellet Substances 0.000 description 1
- 230000036632 reaction speed Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B5/00—Making pig-iron in the blast furnace
- C21B5/008—Composition or distribution of the charge
-
- C—CHEMISTRY; METALLURGY
- C21—METALLURGY OF IRON
- C21B—MANUFACTURE OF IRON OR STEEL
- C21B5/00—Making pig-iron in the blast furnace
- C21B5/006—Automatically controlling the process
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/90—Programming languages; Computing architectures; Database systems; Data warehousing
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention discloses an optimization method of blast furnace steelmaking raw material proportion and an application system thereof, and relates to the technical field of intelligent manufacturing. The method comprises the following steps: and (3) data acquisition: acquiring raw material information, including: real-time weight information of raw materials, composition information of raw materials, supplier information of raw materials, price information of raw materials, and stock information of raw materials; data preprocessing: carrying out data cleaning on the raw material information to obtain preprocessed raw material information, wherein the data cleaning comprises abnormal value removal, data duplication removal, missing value processing and data standardization; establishing an optimization algorithm; taking the pretreated raw material information as input information of the optimization algorithm to obtain optimal raw material proportioning information; and pushing the optimal raw material ratio information to an application terminal, so that the optimal raw material ratio can be accurately found.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing, in particular to an optimization method of blast furnace steelmaking raw material proportion and an application system thereof.
Background
Blast furnace steelmaking is an important process in the steel and foundry industry, requiring the use of a variety of raw materials, most of which need to be purchased from the outside. For some small and medium-sized enterprises, how to meet the process requirements of the products and maximize the economic benefits is the first thing of the enterprises due to unstable raw material supply. Currently, these enterprises commonly adopt a mode of holding a production operation meeting every half month to jointly make a subsequent production plan and a raw material purchasing plan. The working mode has the problems of time and cost waste, influence of human factors on planning accuracy, low reaction speed, incapability of optimizing profits to the greatest extent and the like.
Disclosure of Invention
The invention provides an optimization method of a blast furnace steelmaking raw material ratio and an application system thereof, wherein the optimization method of the blast furnace steelmaking raw material ratio can accurately find the optimal raw material ratio, improve the product quality and stability, effectively improve the production efficiency and reduce the production cost.
According to an aspect of the present disclosure, there is provided a method for optimizing a blast furnace steelmaking raw material ratio, the method comprising:
and (3) data acquisition: acquiring raw material information, including: real-time weight information of raw materials, composition information of raw materials, supplier information of raw materials, price information of raw materials, and stock information of raw materials;
data preprocessing: carrying out data cleaning on the raw material information to obtain preprocessed raw material information, wherein the data cleaning comprises abnormal value removal, data duplication removal, missing value processing and data standardization;
establishing an optimization algorithm, comprising: deducing a calculation formula of the raw material components according to the chemical rules from raw materials to products and the constraint relation among production elements, and deducing an optimization function of the raw material proportion by combining the price elements of the raw materials;
taking the pretreated raw material information as input information of the optimization algorithm to obtain optimal raw material proportioning information;
pushing the optimal raw material proportioning information to an application terminal.
In one possible implementation manner, the calculating formula of the raw material component is deduced according to the chemical rule between raw materials and products and the constraint relation between each production element, and the optimizing function of the raw material proportion is deduced by combining the price elements of the raw materials, including:
the lowest cost objective function is as follows:
（1）
wherein the sintering process comprises n raw materials, the blast furnace process comprises m raw materials, m and n are more than or equal to 1,is the proportion of the ith raw material in the sintering process, < > and the formula of the (I)>Is the total weight of the sintering material, < > and->Is the proportion of the ith raw material in the blast furnace process, < > I->Is the total weight of the blast furnace raw material, < > and->Represents the unit price of the ith sintering material,/-)>The i-th raw material price of the blast furnace is represented, the control variables are the raw material ratio of the sintering process and the raw material ratio of the blast furnace process, and the aim is that the total purchase cost is the lowest.
In one possible implementation manner, the calculating formula of the raw material component is deduced according to the chemical rule between raw materials and products and the constraint relation between the production elements, and the optimizing function of the raw material proportion is deduced by combining the price elements of the raw materials, and the method further comprises:
the raw material composition calculation function in the sintering process is as follows formula (2):
（2）
the raw material composition calculation function in the blast furnace process is as follows formula (3):
（3）
wherein ,represents the content of the j-th component in the raw ore of the sintering process,/-, and>represents the water content of the ith raw material, +.>Represents the content of the j-th element in the i-th material,/-th element>Indicating the burn-out of the raw material;
represents the content of the jth component in the molten iron in the blast furnace process,/-, and>represents the water content of the ith raw material, +.>Represents the content of the j-th element in the i-th raw material.
In one possible implementation manner, the calculating formula of the raw material component is deduced according to the chemical rule between raw materials and products and the constraint relation between the production elements, and the optimizing function of the raw material proportion is deduced by combining the price elements of the raw materials, and the method further comprises:
the constraint condition of the molten iron composition is as shown in formula (4):
（4）
the molten iron constraint condition means that the contents of the components in the final molten iron are within a limited range,a lower limit indicating the content of the j-th component in molten iron,/->An upper limit indicating the content of the j-th component in the molten iron;
and combining the molten iron constraint conditions to obtain a new lowest cost objective function as a formula (5):
（5）
converting the constraint condition into a penalty function, wherein the added term in the formula (5) is the penalty function, and a new objective function F (sp, gp) is obtained, wherein:represents penalty factors,/->Is formula (1),>is formula (3).
In one possible implementation manner, the calculating formula of the raw material component is deduced according to the chemical rule between raw materials and products and the constraint relation between the production elements, and the optimizing function of the raw material proportion is deduced by combining the price elements of the raw materials, and the method further comprises:
iterative formula (6):
（6）
wherein ,is the formula (5) at->Derivative of->Is an iteration step length, and k is a natural number;
and (3) performing iterative calculation through a formula (6), and continuously updating the control variable until the new lowest cost objective function converges, wherein the obtained control variable is used as the optimal sintering process raw material ratio and the optimal blast furnace process raw material ratio.
In one possible implementation, the performing the iterative calculation by the formula (6) continuously updates the control variable until the new lowest cost objective function converges, including:
a. randomly selecting a set of control variables；
b. Calculating the current pointGradient of->I.e. control variables->Deriving a new lowest cost objective function to be input;
c. calculating a new control variable value by using a formula (6);
d. repeating the step b and the step c until the new lowest cost objective function converges;
e. using multiple sets of random control variablesAs a starting point of equation (6), repeating steps b to d, obtaining a plurality of sets of control variables that converge the new lowest cost objective function, and determining an optimal set of control variables therefrom.
According to an aspect of the present disclosure, there is provided an application system for optimizing a blast furnace steelmaking raw material ratio, the application system comprising:
the data acquisition module is used for acquiring real-time weight information of the raw materials through the sensor network;
the data storage module is used for storing the real-time weight information of the raw materials, the component information of the raw materials, the supplier information of the raw materials, the price information of the raw materials and the stock information of the raw materials;
the data processing module is used for carrying out data cleaning on the raw material information to obtain preprocessed raw material information, wherein the data cleaning comprises abnormal value removal, data duplication removal, missing value processing and data standardization, a calculation formula of raw material components is deduced according to chemical rules from raw materials to products and constraint relations among production elements, and then an optimization function of raw material proportion is deduced by combining price elements of the raw materials;
taking the pretreated raw material information as input information of the optimization algorithm to obtain optimal raw material proportioning information;
pushing the optimal raw material proportioning information to an application terminal;
and the application terminal is used for receiving the optimal raw material proportioning information.
Compared with the prior art, the invention has the beneficial effects that:
the optimization method of the blast furnace steelmaking raw material ratio is an algorithm program capable of helping steelmaking enterprises find the raw material ratio with the best economic benefit, and is characterized by establishing a data file, formulating a data acquisition scheme, designing a weight algorithm, deriving a calculation formula (and a derivative function), carrying out iterative calculation to finally obtain the best ratio, packaging the best ratio into an API, deploying the API on a server, calculating a group of best ratios every minute and pushing the best ratios to related operators. The automatic degree of production is improved, the optimal raw material ratio can be quickly found through automatic calculation of an algorithm, the production efficiency is effectively improved, and the production cost is reduced; the real-time performance of production control is improved: the algorithm is deployed on a server, a group of optimal proportions are calculated every minute and are pushed to related operators in real time, so that the change of a production site can be responded in time, and the production efficiency and the flexibility are improved; moreover, the calculation accuracy of the raw material ratio is improved: the algorithm can accurately find the optimal raw material ratio by analyzing and calculating various data in the production process, and improves the quality and stability of the product.
Drawings
FIG. 1 shows a block diagram of a method for optimizing a blast furnace steelmaking raw material ratio in accordance with an embodiment of the present disclosure.
FIG. 2 illustrates a block diagram of an application system for optimizing blast furnace steelmaking material ratios in accordance with an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the disclosure will be described in detail below with reference to the drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
In addition, numerous specific details are set forth in the following detailed description in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art have not been described in detail in order not to obscure the present disclosure.
FIG. 1 shows a block diagram of a method for optimizing a blast furnace steelmaking raw material ratio according to an embodiment of the disclosure, the method comprising:
step S01, data acquisition: acquiring raw material information, including: real-time weight information of the raw material, composition information of the raw material, supplier information of the raw material, price information of the raw material, and stock information of the raw material. For example, all data required for calculation are obtained through various modes of sensors, databases, manual entry and the like according to the real-time performance and the integrity requirement of data acquisition, wherein: acquiring real-time weight, flow rate and position of the raw materials through sensor data; obtaining ingredient inspection information of the raw materials through a database; and acquiring information such as supplier information, price, bin number and the like of the raw materials through manual input of a user.
In one possible implementation, the raw material information may also be obtained by reading a data dictionary of the data archive, e.g., by reading raw material information and process constraint information from the data dictionary, including: dynamic information such as raw material composition, price, stock, consumption, etc., and static information such as product quality requirements, warehouse size, conveyor load, etc.
Step S02, data preprocessing: and carrying out data cleaning on the raw material information to obtain preprocessed raw material information, wherein the data cleaning comprises abnormal value removal, data duplication removal, missing value processing and data standardization.
In one possible implementation, the collected data is sorted and cleaned according to the data archive requirements to avoid errors and noise affecting the accuracy of the algorithm calculation results. And sequencing the data according to the importance and the credibility of the data, adjusting and optimizing according to actual conditions, and finally calculating the numerical value for formula calculation by using a weighted average method.
Step S03, an optimization algorithm is established, which comprises the following steps: deducing a calculation formula of the raw material components according to the chemical rules from raw materials to products and the constraint relation among production elements, and deducing an optimization function of the raw material proportion by combining the price elements of the raw materials;
the lowest cost objective function is as follows:
（1）
wherein the sintering process comprises n raw materials, the blast furnace process comprises m raw materials, m and n are more than or equal to 1,is the proportion of the ith raw material in the sintering process, < > and the formula of the (I)>Is the total weight of the sintering material, < > and->Is the proportion of the ith raw material in the blast furnace process, < > I->Is the total weight of the blast furnace raw material, < > and->Represents the unit price of the ith sintering material,/-)>The i-th raw material price of the blast furnace is represented, the control variables are the raw material ratio of the sintering process and the raw material ratio of the blast furnace process, and the aim is that the total purchase cost is the lowest.
The raw material composition calculation function in the sintering process is as follows formula (2):
（2）
the raw material composition calculation function in the blast furnace process is as follows formula (3):
（3）
wherein ,represents the content of the j-th component in the raw ore of the sintering process,/-, and>represents the water content of the ith raw material, +.>Represents the content of the j-th element in the i-th material,/-th element>The burn-out (combustion loss) of the raw material is shown;
represents the content of the jth component in the molten iron in the blast furnace process,/-, and>represents the water content of the ith raw material, +.>Represents the content of the j-th element in the i-th raw material.
The constraint condition of the molten iron composition is as shown in formula (4):
（4）
the molten iron constraint condition means that the contents of the components in the final molten iron are within a limited range,a lower limit indicating the content of the j-th component in molten iron,/->An upper limit indicating the content of the j-th component in the molten iron;
and combining the molten iron constraint conditions to obtain a new lowest cost objective function as a formula (5):
（5）
converting the constraint condition into a penalty function, wherein the added term in the formula (5) is the penalty function, and a new objective function F (sp, gp) is obtained, wherein:represents penalty coefficient, selects according to actual situation, < ->Is formula (1),>is formula (3).
Iterative formula (6):
（6）
wherein ,is the formula (5) at->Derivative of->Is the iteration step length, k is a natural number,represents the k-th set of control variables,>represents the k+1th set of control variables;
and (3) performing iterative calculation through a formula (6), and continuously updating the control variable until the new lowest cost objective function converges, wherein the obtained control variable is used as the optimal sintering process raw material ratio and the optimal blast furnace process raw material ratio.
In one possible implementation, the performing the iterative calculation by the formula (6) continuously updates the control variable until the new lowest cost objective function converges, including:
a. randomly selecting a set of control variables；
b. Calculating the current pointGradient of->I.e. control variables->Deriving a new lowest cost objective function to be input;
c. calculating a new control variable value by using a formula (6);
d. repeating the step b and the step c until the new lowest cost objective function converges;
e. using multiple sets of random control variablesAs a starting point of equation (6), repeating steps b to d, obtaining a plurality of sets of control variables that converge the new lowest cost objective function, and determining an optimal set of control variables therefrom.
Step S04, taking the preprocessed raw material information as input information of the optimization algorithm to obtain optimal raw material proportioning information;
and step S05, pushing the optimal raw material proportioning information to an application terminal.
FIG. 2 illustrates a block diagram of an application system for optimizing blast furnace steelmaking material ratios, as shown in FIG. 2, according to one embodiment of the disclosure, the application system comprising:
and the data acquisition module is used for acquiring real-time weight information of the raw materials through the sensor network. For example, the feedstock may include: rich mineral powder, concentrate powder, internal return mineral, pellet, coke and the like.
And the data storage module is used for storing the real-time weight information of the raw materials, the ingredient information of the raw materials, the supplier information of the raw materials, the price information of the raw materials and the stock information of the raw materials. For example, the data storage module may be a storage server in the cloud.
The data processing module is used for carrying out data cleaning on the raw material information to obtain preprocessed raw material information, wherein the data cleaning comprises abnormal value removal, data duplication removal, missing value processing and data standardization, a calculation formula of raw material components is deduced according to chemical rules from raw materials to products and constraint relations among production elements, and then the price elements of the raw materials are combined to deduce an optimization function of the raw material proportion.
Taking the pretreated raw material information as input information of the optimization algorithm to obtain optimal raw material proportioning information;
pushing the optimal raw material proportioning information to an application terminal;
and the application terminal is used for receiving the optimal raw material proportioning information.
For example, the optimization algorithm may be packaged as an API (Application Programming Interfac, application programming interface) for deployment onto a server, and the data processing module may be a server.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present disclosure, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units described above may be implemented either in hardware or in software program modules.
The integrated units, if implemented in the form of software program modules, may be stored in a computer-readable memory for sale or use as a stand-alone product. Based on such understanding, the technical solution of the present disclosure may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in the various embodiments of the present disclosure. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has described in detail embodiments of the present disclosure, with specific examples being employed herein to illustrate the principles and implementations of the present disclosure, the above examples being provided solely to assist in understanding the methods of the present disclosure and their core ideas; meanwhile, as one of ordinary skill in the art will have variations in the detailed description and the application scope in light of the ideas of the present disclosure, the present disclosure should not be construed as being limited to the above description.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (7)
1. The optimization method of the blast furnace steelmaking raw material proportion is characterized by comprising the following steps:
and (3) data acquisition: acquiring raw material information, including: real-time weight information of raw materials, composition information of raw materials, supplier information of raw materials, price information of raw materials, and stock information of raw materials;
data preprocessing: carrying out data cleaning on the raw material information to obtain preprocessed raw material information, wherein the data cleaning comprises abnormal value removal, data duplication removal, missing value processing and data standardization;
establishing an optimization algorithm, comprising: deducing a calculation formula of the raw material components according to the chemical rules from raw materials to products and the constraint relation among production elements, and deducing an optimization function of the raw material proportion by combining the price elements of the raw materials;
taking the pretreated raw material information as input information of the optimization algorithm to obtain optimal raw material proportioning information;
pushing the optimal raw material proportioning information to an application terminal.
2. The method for optimizing the raw material ratio of the blast furnace steelmaking according to claim 1, wherein the step of deriving a calculation formula of raw material components according to chemical rules from raw materials to products and constraint relations among production elements, and deriving an optimization function of the raw material ratio by combining price elements of the raw materials comprises the following steps:
the lowest cost objective function is as follows:（1）
wherein the sintering process comprises n raw materials, the blast furnace process comprises m raw materials, m and n are more than or equal to 1,is the proportion of the ith raw material in the sintering process, < > and the formula of the (I)>Is the total weight of the sintering material, < > and->Is the proportion of the ith raw material in the blast furnace process, < > I->Is the total weight of the blast furnace raw material, < > and->Represents the unit price of the ith sintering material,/-)>The i-th raw material price of the blast furnace is represented, the control variables are the raw material ratio of the sintering process and the raw material ratio of the blast furnace process, and the aim is that the total purchase cost is the lowest.
3. The optimization method of the blast furnace steelmaking raw material ratio according to claim 2, wherein the calculation formula of the raw material components is deduced according to the chemical rules from raw materials to products and the constraint relation among production elements, and the optimization function of the raw material ratio is deduced by combining the price elements of the raw materials, and the optimization method further comprises the steps of:
the raw material composition calculation function in the sintering process is as follows formula (2):
（2）
the raw material composition calculation function in the blast furnace process is as follows formula (3):
（3）
wherein ,represents the content of the j-th component in the raw ore of the sintering process,/-, and>represents the water content of the ith raw material, +.>Represents the content of the j-th element in the i-th material,/-th element>Indicating the burn-out of the raw material;
represents the content of the jth component in the molten iron in the blast furnace process,/-, and>the water content of the i-th raw material is shown,represents the content of the j-th element in the i-th raw material.
4. The optimization method of the blast furnace steelmaking raw material ratio according to claim 3, wherein the calculation formula of the raw material components is deduced according to the chemical rules from raw materials to products and the constraint relation among production elements, and the optimization function of the raw material ratio is deduced by combining the price elements of the raw materials, and the optimization method further comprises the following steps:
the constraint condition of the molten iron composition is as shown in formula (4):
（4）
the molten iron constraint condition means that the contents of the components in the final molten iron are within a limited range,a lower limit indicating the content of the j-th component in molten iron,/->An upper limit indicating the content of the j-th component in the molten iron;
and combining the molten iron constraint conditions to obtain a new lowest cost objective function as a formula (5):
（5）
converting the constraint condition into a penalty function, wherein the added term in the formula (5) is the penalty function, and a new objective function F (sp, gp) is obtained, wherein:represents penalty factors,/->Is formula (1),>is formula (3).
5. The method for optimizing the raw material ratio in the blast furnace steelmaking according to claim 4, wherein the calculation formula of the raw material components is deduced according to the chemical rules from raw materials to products and the constraint relation among production elements, and the optimization function of the raw material ratio is deduced by combining price elements of the raw materials, and the method further comprises:
iterative formula (6):
（6）
wherein ,is the formula (5) at->Derivative of->Is an iteration step length, and k is a natural number;
and (3) performing iterative calculation through a formula (6), and continuously updating the control variable until the new lowest cost objective function converges, wherein the obtained control variable is used as the optimal sintering process raw material ratio and the optimal blast furnace process raw material ratio.
6. The method according to claim 5, wherein the iterative calculation is performed by the formula (6), and the control variables are continuously updated until the new lowest cost objective function converges, comprising:
a. randomly selecting a set of control variables；
b. Calculating the current pointGradient of->I.e. control variables->Deriving a new lowest cost objective function to be input;
c. calculating a new control variable value by using a formula (6);
d. repeating the step b and the step c until the new lowest cost objective function converges;
e. using multiple sets of random control variablesAs a starting point of equation (6), repeating steps b to d, obtaining a plurality of sets of control variables that converge the new lowest cost objective function, and determining an optimal set of control variables therefrom.
7. An application system for optimizing the proportion of raw materials for blast furnace steelmaking, which is characterized by comprising:
the data acquisition module is used for acquiring real-time weight information of the raw materials through the sensor network;
the data storage module is used for storing the real-time weight information of the raw materials, the component information of the raw materials, the supplier information of the raw materials, the price information of the raw materials and the stock information of the raw materials;
the data processing module is used for carrying out data cleaning on the raw material information to obtain preprocessed raw material information, wherein the data cleaning comprises abnormal value removal, data duplication removal, missing value processing and data standardization, a calculation formula of raw material components is deduced according to chemical rules from raw materials to products and constraint relations among production elements, and then an optimization function of raw material proportion is deduced by combining price elements of the raw materials;
taking the pretreated raw material information as input information of the optimization algorithm to obtain optimal raw material proportioning information;
pushing the optimal raw material proportioning information to an application terminal;
and the application terminal is used for receiving the optimal raw material proportioning information.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BE684315A (en) * | 1965-09-10 | 1967-01-19 | ||
| CN102722652A (en) * | 2012-06-01 | 2012-10-10 | 攀钢集团攀枝花钢钒有限公司 | Blast furnace smelting cost calculating and optimizing method |
| CN104593532A (en) * | 2015-01-19 | 2015-05-06 | 河北联合大学 | Furnace burden optimization method for iron-making system |
| CN104975118A (en) * | 2015-05-25 | 2015-10-14 | 王鹏 | Method for optimizing ratio of raw materials before iron making |
| JP2018062681A (en) * | 2016-10-12 | 2018-04-19 | 新日鐵住金株式会社 | Method of estimating mixture ratio of raw material in blast furnace |
| KR101951157B1 (en) * | 2018-09-13 | 2019-02-21 | 마영모 | The automatic preparation system for food material and operating method thereof |
| CN110867219A (en) * | 2019-10-23 | 2020-03-06 | 同济大学 | Sintering material optimization control method and device based on ISAA algorithm |
| CN114819409A (en) * | 2022-06-21 | 2022-07-29 | 中国科学院自动化研究所 | Intelligent optimization method and system for sintering ore blending |
| CN114864010A (en) * | 2022-03-07 | 2022-08-05 | 马鞍山钢铁股份有限公司 | Multifunctional ore blending model building method for pellet ore |
| CN115359851A (en) * | 2022-07-18 | 2022-11-18 | 浙江大学 | Multi-objective prediction optimization method for sintering burdening based on extreme random tree-NSGA-II |
| CN115565618A (en) * | 2022-09-21 | 2023-01-03 | 中冶南方工程技术有限公司 | Multi-objective optimization method for blast furnace burden, terminal equipment and storage medium |
-
2023
- 2023-08-16 CN CN202311029476.3A patent/CN116759032B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BE684315A (en) * | 1965-09-10 | 1967-01-19 | ||
| CN102722652A (en) * | 2012-06-01 | 2012-10-10 | 攀钢集团攀枝花钢钒有限公司 | Blast furnace smelting cost calculating and optimizing method |
| CN104593532A (en) * | 2015-01-19 | 2015-05-06 | 河北联合大学 | Furnace burden optimization method for iron-making system |
| CN104975118A (en) * | 2015-05-25 | 2015-10-14 | 王鹏 | Method for optimizing ratio of raw materials before iron making |
| JP2018062681A (en) * | 2016-10-12 | 2018-04-19 | 新日鐵住金株式会社 | Method of estimating mixture ratio of raw material in blast furnace |
| KR101951157B1 (en) * | 2018-09-13 | 2019-02-21 | 마영모 | The automatic preparation system for food material and operating method thereof |
| CN110867219A (en) * | 2019-10-23 | 2020-03-06 | 同济大学 | Sintering material optimization control method and device based on ISAA algorithm |
| CN114864010A (en) * | 2022-03-07 | 2022-08-05 | 马鞍山钢铁股份有限公司 | Multifunctional ore blending model building method for pellet ore |
| CN114819409A (en) * | 2022-06-21 | 2022-07-29 | 中国科学院自动化研究所 | Intelligent optimization method and system for sintering ore blending |
| CN115359851A (en) * | 2022-07-18 | 2022-11-18 | 浙江大学 | Multi-objective prediction optimization method for sintering burdening based on extreme random tree-NSGA-II |
| CN115565618A (en) * | 2022-09-21 | 2023-01-03 | 中冶南方工程技术有限公司 | Multi-objective optimization method for blast furnace burden, terminal equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 贺建峰: ""烧结配料计算新方法"", 《山东冶金》, vol. 17, no. 1 * |
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